182 intercept rate while low-confidence zones were withheld from the drill program and directed to supplementary data acquisition. At the Australian and Kazakh orogenic gold sites, the approach confirmed economic mineralization in volumes undetected by conventional estimation, with the PSA adapting to local data quality conditions. The principal operational implication is that mines are routinely collecting geological logging and core observation data that contain genuine predictive value for resource estimation and that this value is currently not being captured by conventional workflows. The technical tools required to extract this value have been developed and validated across multiple deposit types. No new data collection programs are required for implementation; the marginal cost of application is therefore substantially lower than the marginal cost of conventional grade control drilling. Identified priorities for future research include: expansion of prospective validation to additional deposit types, specifically VMS, porphyry, and sediment-hosted systems; incorporation of geochemical and geotechnical data streams beyond lithological logging; and the development of standardized geological logging quality assurance protocols designed to support ML model integration, recognizing that model performance is bounded above by the information content and consistency of the logging data on which it trains. Continued accumulation of operational track record also remains essential—the methodology demonstrates technical transferability across geological contexts, but institutional confidence in model outputs is built site by site through demonstrated field performance. ACKNOWLEDGEMENTS The authors acknowledge S.C.M. Atacama Kozan for access to the mine database and discussions with Katsuhito Terashima. Thanks to Ady Aguilar for her help with figure preparation and to the anonymous reviewers who provided feedback on this work. REFERENCES del Real, I., Thompson, J.F.H. and Carriedo, J., 2018. Lithological and structural controls on the genesis of the Candelaria–Punta del Cobre Iron Oxide Copper Gold district, Northern Chile. Ore Geology Reviews, 102, pp.106–153. del Real, I., Thompson, J., Simon, A. and Reich, M., 2023. Formation of giant iron oxide-copper-gold deposits by superimposed episodic hydrothermal pulses. Scientific Reports, 13, 12041. Dumakor-Dupey, N.K. and Arya, S., 2021. Machine learning—a review of applications in mineral resource estimation. Energies, 14(14), 4079. First, D.M., Sucholutsky, I., Mogilny, D. and Yusufali, F., 2023. Introducing deep learning and interpreting the patterns—a mineral deposit perspective. In: Proceedings of the Mineral Resource Estimation Conference 2023, pp.2–14. Melbourne: The Australasian Institute of Mining and Metallurgy. Glacken, I.M. and Blackney, P.C.J., 2022. Categorical and multiple indicator Kriging—are we ignoring the geology? In: Proceedings of the 12th International Mining Geology Conference 2022, pp.67–77. Melbourne: The Australasian Institute of Mining and Metallurgy. Glacken, I., Rondon, O. and Levett, J., 2023. Drill hole spacing analysis for classification and cost optimisation—a critical review of techniques. In: Proceedings of the Mineral Resource Estimation Conference 2023, pp.179–191. Melbourne: The Australasian Institute of Mining and Metallurgy. Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep Learning. Cambridge, MA: MIT Press.
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